Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks.

M G Ford1, W R Pitt, D C Whitley

  • 1Centre for Molecular Design, IBBS, University of Portsmouth, King Henry Building, King Henry I St., Portsmouth PO1 2DY, UK. martyn.ford@port.ac.uk

Journal of Molecular Graphics & Modelling
|June 9, 2004
PubMed
Summary

Researchers used machine learning to identify potential kinase inhibitors. This method effectively screened purchased compounds, achieving results comparable to known active molecules for drug discovery.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes.

The Science of the total environment·2016
Same author

Vicinity analysis: a methodology for the identification of similar protein active sites.

Journal of molecular modeling·2008
Same author

QSAR studies using the parashift system.

SAR and QSAR in environmental research·2008
Same author

Paediatric research in emergency departments international collaborative (predict).

Journal of paediatrics and child health·2006
Same author

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for alpha2-adrenoceptors.

Journal of computer-aided molecular design·2005
Same author

Childhood poisoning in Queensland: an analysis of presentation and admission rates.

Journal of paediatrics and child health·2002

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Protein kinases are a major class of drug targets.
  • Identifying novel kinase inhibitors is crucial for drug discovery.
  • Existing methods for screening compounds can be resource-intensive.

Purpose of the Study:

  • To develop a computational method for identifying potential kinase inhibitors.
  • To assess the effectiveness of machine learning in screening purchased compounds.
  • To integrate this approach into early-stage drug discovery pipelines.

Main Methods:

  • Applied linear discriminant analysis and a committee of neural networks.
  • Utilized the MDDR database for compound selection.
  • Employed BCUT parameters as molecular descriptors encoding structural and interaction information.

Related Experiment Videos

Main Results:

  • The developed technique successfully identified compounds that inhibit kinases.
  • Screened compounds achieved hit rates comparable to known actives for related targets.
  • The approach proved effective for selecting screening candidates and compound purchases.

Conclusions:

  • Machine learning models, using BCUT parameters, can effectively identify kinase inhibitors.
  • This computational approach serves as a valuable filter in early drug discovery.
  • The method aids in prioritizing compound purchases and synthetic efforts for kinase targets.